Genome-wide Marker-assisted Selection Research Articles

Rapid breeding of ‘Natsuiro’, a new rice cultivar with resistance to high temperatures and rice blast, using genome-wide marker-assisted selection

我が国の稲作経営においては，生産コストおよび環境負荷を低減しつつ高値販売が安定して実現できるイネ品種が求められている．高温登熟性と病害抵抗性を兼ね備え，稲作経営からのニーズに応えることができる早生品種は，現状ではまだ少ない．「なついろ」は，早生で高温登熟性に優れる「三重23号」の遺伝背景に，「ともほなみ」由来のいもち病圃場抵抗性遺伝子pi21を短期間で導入することにより育成された．連続戻し交雑では，BC1F1からBC3F1の各世代において，pi21を対象としたDNAマーカー選抜に加えゲノムワイドマーカーを用いた背景選抜を実施することにより，最初の人工交配（2013年）から2年2ヶ月で目的の遺伝子型を実現し，その後系統選抜および生産力検定試験等の特性把握調査の後に，6年後の2019年に品種登録出願された．「なついろ」は「三重23号」と比較して，いもち病圃場抵抗性が強いこと，稈長と穂長がやや長いこと，千粒重がやや小さいこと以外は概ね同様の農業形質を有する．また，「コシヒカリ」と比較して，稈長は10 cm短く，穂数はやや少なく，草型は“中間型”である．出穂期，成熟期は「コシヒカリ」よりそれぞれ3日および5日早い．pi21を有し，葉いもち圃場抵抗性は“極強”である．高温登熟性は「三重23号」と同程度の“強”で，「コシヒカリ」より強い．「なついろ」は2020年に三重県の奨励品種として採用された．高温登熟性と病害抵抗性を兼ね備え，稲作経営からのニーズに応えることができる早生品種としての普及が期待される．

Development and use of chromosome segment substitution lines as a genetic resource for crop improvement

CSSLs are a complete library of introgression lines with chromosomal segments of usually a distant genotype in an adapted background and are valuable genetic resources for basic and applied research on improvement of complex traits. Chromosome segment substitution lines (CSSLs) are genetic stocks representing the complete genome of any genotype in the background of a cultivar as overlapping segments. Ideally, each CSSL has a single chromosome segment from the donor with a maximum recurrent parent genome recovered in the background. CSSL development program requires population-wide backcross breeding and genome-wide marker-assisted selection followed by selfing. Each line in a CSSL library has a specific marker-defined large donor segment. CSSLs are evaluated for any target phenotype to identify lines significantly different from the parental line. These CSSLs are then used to map quantitative trait loci (QTLs) or causal genes. CSSLs are valuable prebreeding tools for broadening the genetic base of existing cultivars and harnessing the genetic diversity from the wild- and distant-related species. These are resources for genetic map construction, mapping QTLs, genes or gene interactions and their functional analysis for crop improvement. In the last two decades, the utility of CSSLs in identification of novel genomic regions and QTL hot spots influencing a wide range of traits has been well demonstrated in food and commercial crops. This review presents an overview of how CSSLs are developed, their status in major crops and their use in genomic studies and gene discovery.

Identification of forty-five gene-derived polymorphic microsatellite loci for the sea cucumber, Apostichopus japonicus

Sea cucumber (Apostichopus japonicus) is a common benthic detritus feeder of coastal sea habitats of East Asia, including Japan, China, Korea and fareastern Russia (Chang et al. 2009). Because of its high nutritive value and use in traditional medicine, sea cucumber is considered as one of the most valuable species in aquaculture markets in China. However, the number of wild sea cucumbers has severely declined owing to overexploitation and environment deterioration (Peng et al. 2009). To increase production of sea cucumber, artificial breeding techniques were established in China in the 1980s and sea cucumber aquaculture has been developing rapidly in recent years (Qiao and Cheng 2005). Because sea cucumber is an important aquaculture species in China, application of marker-assisted selection (MAS) or genomewide marker-assisted selection (G-MAS) in the A. japonicus breeding programme is expected to be a fertile research area. To apply MAS and G-MAS, a saturated linkage map should be constructed first, using large numbers of molecular markers. Unfortunately, progress in genetic and genomic research in sea cucumber has relatively slower than in other economically important marine species (Cecilia et al. 2010; Guo et al. 2011; Wang et al. 2011; Zhang et al. 2011). There is only one preliminary genetic map based on 37 amplified fragment length polymorphism (AFLP) primer combinations and 20 microsatellite (simple sequences repeat; SSR) markers from 88 individuals (Li et al. 2009). While this framework linkage map is useful, the marker density is still low. Mapping additional markers, especially appropriate codominant markers, is particularly important. Some microsatellite markers (Kanno et al. 2005; Zhan et al. 2007; Peng et al. 2009; Liao et al. 2011) and single-nucleotide polymorphism (SNPs) (Sun et al. 2010; Yang et al. 2012) have been developed for sea cucumber. However, the number

Genome-wide selection in poultry

In poultry breeding programs owned by private companies, selection is done within closed populations based on comprehensive phenotypic data recording in both pure and cross line birds under standardised housing conditions. Due to sex-limited data recording, male selection for egg quality and production traits is based mainly on female sibling tests. Early selection of the most promising male within full sib families will improve the rate of genetic progress and can substantially reduce the generation interval. Several past studies, based mainly on microsatellites, have identified quantitative trait loci (QTL) for production and quality traits with only limited use in commercial programs. Genome-wide selection is still in its initial stages in which 10–40-K single nucleotide polymorphism (SNP) chips have been used so far. Due to sequencing of all major pure lines from DNA pools, a customised 600-K SNP chip has been developed for comprehensive genotyping of all commercial lines. Small-scale line-specific SNP chips will be developed afterwards in order to reduce costs for genotyping of male progeny during rearing periods. Only the most promising young males will be transferred to the breeding farm for performance testing and pedigree reproduction. Parental generation will still be genotyped with the comprehensive SNP chip and used for retraining and for imputing. The first results using 30-K SNP chips were obtained from a commercial line used for training, validation and selection, which have shown improved accuracy of prediction at a young age and so resulted in increased genetic gain. Genome-wide marker-assisted selection must prove its advantages over traditional methods including cost benefits.

Bias in genomic predictions for populations under selection

Prediction of genetic merit or disease risk using genetic marker information is becoming a common practice for selection of livestock and plant species. For the successful application of genome-wide marker-assisted selection (GWMAS), genomic predictions should be accurate and unbiased. The effect of selection on bias and accuracy of genomic predictions was studied in two simulated animal populations under weak or strong selection and with several heritabilities. Prediction of genetic values was by best-linear unbiased prediction (BLUP) using data either from relatives summarized in pseudodata for genotyped individuals (multiple-step method) or using all available data jointly (single-step method). The single-step method combined genomic- and pedigree-based relationship matrices. Predictions by the multiple-step method were biased. Predictions by a single-step method were less biased and more accurate but under strong selection were less accurate. When genomic relationships were shifted by a constant, the single-step method was unbiased and the most accurate. The value of that constant, which adjusts for non-random selection of genotyped individuals, can be derived analytically.

Genomic study of the response of chicken to highly pathogenic avian influenza virus.

BackgroundThe host mounts an immune response to pathogens, but few data are currently available on the role of host genetics in variation in response to avian influenza (AI). The study presented here investigated the role of the host genetic background in response to in vivo infection with AI virus (AIV).MethodsExperimental lines of chicken and commercial crosses were experimentally infected intratracheally with 103 EID50/bird of A/Chicken/Italy/13474/99 H7N1 highly pathogenic avian influenza virus (HPAIV). Chickens were genotyped for the Mx polymorphism causing the S631N mutation, and for the Major Histocompatibility Complex (MHC). Whole-genome genotyping was carried out using 60 k Single Nucleotide Polymorphism (SNP) array developed by the poultry Genome-Wide Marker-Assisted Selection Consortium (GWMASC).ResultsVariability in response of different chicken lines to the HPAIV infections and some degree of resistance to AI were observed: a statistically significant effect of chicken line on the response to infection was found. There was no association between survival in healthy conditions and polymorphisms at the Mx gene and the MHC-B region. The analysis based on the 60 k SNPs provided a good clustering of the chicken lines, but no specific genetic cluster associated with response to AIV was identified.ConclusionsNeither the genotype at the Mx gene or MHC-B locus, nor for SNP spanning the whole-genome identified loci involved in variations to response to AIV infection. These results point towards the possibility that either the genetic factors affecting the response of chickens to the H7N1 HPAIV are weak, or relevant alleles were not segregating in the studied populations.

Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: An example using broiler chickens

Data of broiler chickens for 2 pure lines across 3 generations were used for genomic evaluation. A complete population (full data set; FDS) consisted of 183,784 and 164,246 broilers for the 2 lines. The genotyped subsets (SUB) consisted of 3,284 and 3,098 broilers with 57,636 SNP. Genotyped animals were preselected based on more than 20 traits with different index applied to each line. Three traits were analyzed: BW at 6 wk (BW6), ultrasound measurement of breast meat (BM), and leg score (LS) coded 1 = no and 2 = yes for leg defect. Some phenotypes were missing for BM. The training population consisted of the first 2 generations including all animals in FDS or only genotyped animals in SUB. The validation data set contained only genotyped animals in the third generation. Genetic evaluations were performed using 3 approaches: 1) phenotypic BLUP, 2) extending BLUP methodologies to utilize pedigree and genomic information in a single step (ssGBLUP), and 3) Bayes A. Whereas BLUP and ssGBLUP utilized all phenotypic data, Bayes A could use only those of the genotyped subset. Heritabilities were 0.17 to 0.20 for BW6, 0.30 to 0.35 for BM, and 0.09 to 0.11 for LS. The average accuracies of the validation population with BLUP for BW6, BM, and LS were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FDS. With ssGBLUP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FDS, respectively. With Bayes A, the accuracies were 0.60, 0.36, and 0.09 with SUB. With SUB, Bayes A and ssGBLUP had similar accuracies. For traits of high heritability, the accuracy of Bayes A/SUB and ssGBLUP/FDS were similar, and up to 50% better than BLUP/FDS. However, with low heritability, ssGBLUP/FDS was 4 to 6 times more accurate than Bayes A/SUB and 50% better than BLUP/FDS. An optimal genomic evaluation would be multi-trait and involve all traits and records on which selection is based.